Eight Themes From The Corner Of Interoperability And Precision Medicine

This week, I had the opportunity to attend (and participate in) a policy conference focused on interoperability and precision medicine sponsored by the HL7 standards association. Below, I briefly discuss a eight key themes the meeting covered or evoked.

1. Waiting For Interoperability

Listening to key stakeholders provide updates on interoperability, I felt like an audience member watching an existential drama.

Perhaps it was a modern take on Waiting For Godot (“Let’s share data.” They do not move.). It could be an update of No Exit, with hospitals, vendors and policy makers cast as the tortured souls stuck with each other in the same dialogue, forever.

There was certainly little sense that salvation was on the way, or that any person, organization, legislation or higher authority was likely to arrive and meaningfully intervene.

(On the other hand: the future looks extremely bright for working groups.)

From the perspective of most care systems, the idea of interoperability – the easy movement of data between different health systems – must seem almost laughable, and so far removed from the quotidian problems of wrangling the data the care system needs for day-to-day operations and billing, much less anything approaching the grander vision of a learning hospital (where today’s experiences are leveraged to inform tomorrow’s decisions).

When you hear about the colossal struggles required to make even seemingly basic changes -- like ensuring there’s a line on a claims form to report the specific model of medical device implanted (an example shared with me by a representative from the Pew Charitable Trust) -- and when you hear speaker after speaker emphasize the need to “move cautiously” and to “let things marinate,” you get a visceral feel for the pace of change in this area, and perhaps, implicitly, how the prospect of change tends to be viewed.

2. What Is The Problem To Be Solved?

It’s important to understand what problem hospitals – and the EMR vendors who provision them – are trying to solve. EMRs represent a huge financial investment, made in expectation of delivering quantifiable financial benefit. Traditionally, this meant ensuring that all billable work is tracked and documented.

Over time, as healthcare moves gingerly towards an increased focus on quality and value, data collection needs have evolved. Hospitals have a vested interest in understanding what’s happening inside them, at least to the extent needed to evaluate and improve performance across the metrics against which they’ll be assessed (and potentially penalized).

The increased emphasis on quality metrics helps patients to the extent that population-level metrics effectively reflect the care provided to individual patients – a topic of considerable debate, including at this HL7 meeting.

3. Data Scientist Perspective

From the perspective of many data scientists – as well as many health researchers and clinicians – healthcare’s current approach to information gathering seems like a glaring missed opportunity. In Silicon Valley, the mantra is: collect data -> aggregate data -> analyze data, and use the insights gleaned to make life better (by personalizing someone’s Facebook feed, for example) while also making money (e.g. by selling targeted ads).

How much better medicine could be, tech visionaries have argued, if only we had more and better data on each patient, and could integrate learnings, experiences, and genomes (disclosure/reminder, I’m Chief Medical Officer of DNAnexus, a cloud genomics company) across hospitals, states, and perhaps even nations?

Yet while data scientists may see healthcare providers as essentially data capture agents, and wish that far more information was collected, most doctors are already fearful they’re devolving into data entry clerks. Providers have little appetite for entering (or reviewing) any more information than what’s already required to ensure appropriate care and billing -- especially when the payoff for richer documentation and broader data collection (e.g. from wearables) can seem theoretical, vague, and destined to occur in the unspecified future, if at all.

This is the key point: the promise of data science for medicine is that, if you collect all possible data and stir it in a vat, profound insights will emerge that will improve human health.

To date, most practicing physicians have seen abundant evidence around the burden of increased data collection, and very little tangible evidence that doing this results in improved care for their patients. If anything, there’s a palpable sense that their ability to care for patients – to listen and spend time with them (i.e. old-school data collection and analysis) – has been significantly reduced because of the existing documentation requirements.

From the perspective of just about everyone in healthcare, it seems like we’re giving more than we’re getting, contributing lots of information (often the same information, again, and again and again…) yet seeing very little value accrue, leaving us feeling like Obie in Alice’s Restaurant, clutching the 8x10 color glossy pictures in front of the judge with the seeing-eye dog.

Dave Chase famously wrote that “patients are more than a vessel for billing codes,” the data type arguably most important to hospitals and the EMRs. I suppose the next question is whether patients see themselves as collections of genetic and phenotypic data, available for researchers to capture and analyze, and for hospitals to capture and monetize. The answer will inevitably depend upon what patients perceive as the cost, and the benefit they feel they are getting in return.

4. State of Precision Medicine

This brings us to the current state of precision medicine, which after enjoying a period of inflated expectations, now seems to be transitioning (quickly, I hope) through a predictable trough of disillusionment – much as I discussed here, and also spoke about at the meeting.

The skepticism is understandable; while there are compelling examples of early promise, especially in reproductive health and undiagnosed diseases (see here), the truth is that we’re very much still in an information gathering stage. We’re trying to figure out what’s in our genomes, our lab values, our patterns of behavior – and what are the clinically relevant insights to be gleaned from integrating all these data types – many of which we’re only beginning to learn how to properly collect and usefully report.

Additional important efforts here include the Regeneron/Geisinger collaboration (profiled in the New York Timeshere; disclosure: DNAnexus is foundationally involved) and the work of Craig Venter’s company, Human Longevity Inc. (HLI), which explicitly aspires to build “the world’s largest genotype/phenotype database.” The key issues here will be whether Regeneron is ultimately able to use these data to identify promising new drugs, whether Geisinger is able to use these data to deliver improved patient care and better healthcare value, and whether HLI is able both to construct the database they envision and then extract sufficient value to drive a business.

5. Usability vs Privacy

Two key topics at the HL7 meeting involved usability (which leading EMRs are generally described as lacking) and privacy (an emphatic priority of the current system – although some advocates emphasize the need to take patient privacy even more seriously).

In listening to Deborah Peel – a physician and prominent “privacy warrior,” as she describes herself – review the astonishing amount of healthcare data that’s already collected about us, and the potential harm this could cause (see this 2014 slide deck she posted), it felt a world away from the happy place Marc Andreessen and others tend to depict, where the costs of giving up a little privacy (in the view of many in Silicon Valley) generally pale in comparison to the benefits received.

Yet, when I went back to listened to comments from Andreessen more closely, I realized he and Peel might not be so far apart. Andreessen, for instance, explicitly calls out the “trade-off” between privacy and usability, a relationship that seems something like the guns/butter curve of economics. If you want to maximize usability, you need to give up a lot of privacy; if you want to zealously guard privacy, usability will suffer.

Where both Andreessen and Peel seem to agree, however, is in the view that your data belong to you, and shouldn’t be appropriated without explicit individual permission and a clear sense of how it will and won’t be used. “People need to be in control,” Andreessen says, “and make decisions in a conscious way.”

Rick Ratliff, a global managing director of Accenture, presented an example that highlighted the usability end of the spectrum – Disney’s use of “Magic Bands” – tech-enabled bracelets park patrons can choose to wear, and which broadcast your location and preferences to thousands of embedded sensors. The result: rapid access to park activities, personalized interactions with park characters (who might greet a child by name, for instance), convenient payment (similar to Apple Watch), and a personalized collection of photos for you at the park, taken of you by the many park cameras embedded in the landscape. (This is a terrific recent Wired article on the experience and technology.)

Of course, in exchange for this “frictionless” experience, you give up a huge amount of information to Disney, which can now track your movements and purchases with striking granularity. (Conversely, you can opt out – and wait in longer lines, use your time less efficiently, and have a less personalized experience.)

The question, of course, is where should healthcare be on this usability/privacy curve? If I’ve understood Peel correctly, I believe she’d say, emphatically, “this choice belongs to the patient, but the default should be maximal privacy.” If a patient then chooses to donate her data to an organization, or a company – that’s entirely her right, so long as she clearly understand how the data will be used.

6. Do Most Patients Want To Manage Their Data?

A critical question that healthcare is actively wrestling with is whether most patients really do want to manage their health data – a tension I recently discussed.

You can imagine a world in which all our healthcare data were clearly owned and managed by each of us – we might each have individual health data accounts, say. Information gleaned from various interactions with providers, pharmacists, chiropractors, and fitness centers would be routinely deposited into our accounts, as a routine part of service delivery. We could then choose whether to share some or all of our data with research organizations, either for free or for a price. Such a system would clearly place the patient/participant at the center.

Less certain is whether (despite the “free the data” rhetoric) most of us really want to take this on. For some patients and families dealing with complex or life-threatening conditions, the answer is obviously yes – and many people in this category already try to gather and organize all their own data, an inordinately difficult task in the current system (see my recent Wall Street Journalreview of The Patient’s Playbook for additional perspective.)

But while most patients presumably want the right to access their data, it’s far less certain they want to do this for themselves, the way they manage their finances, say. In fact, building something like a Mint.com, a health dashboard for consumers, was a key part of the original vision for both Google Health (before it shut down) and Keas (before it pivoted).

What happened? As Wade Roush nicely described in a profile of entrepreneur Adam Bosworth, a founder of both Google Health and Keas, the Keas team made an important discovery:

Data wasn’t the answer. The Mint-like approach, Bosworth had realized, was working more like a stick than a carrot. “All these people would enter their height and weight and lab data, and immediately we would tell them, ‘You suck. You’re overweight, your blood pressure is too high, your cholesterol is too high, you must change.’ They were gone in 60 seconds,” says Bosworth. “They know what it’s doing to their life expectancy, and they still are not doing the right thing.”

In other words, while monitoring your health data may be appealing if you’re really healthy or really sick, for most of us, it’s more like a memento morimotif, reminding us that we are vulnerable and could pass at any minute. Not surprisingly, many prefer to think about something else, and are happy to leave their data – and anxieties – at the doctor’s office.

7. Innovation vs Regulation

One of the most interesting exchanges at HL7 involved Representative Michael Burgess (R-TX), who practiced medicine for nearly thirty years in Texas before running for office about 12 years ago. In his remarks, Burgess emphasized his concern about excessive regulation dampening innovation, especially around laboratory-developed tests (a worry also voiced by Robb Walton, representing Senator Bill Cassidy [R-LA]).

I asked Burgess afterwards how he thought about the balance between cultivating innovation and ensuring the accuracy and reliability of tests. His response: “I want my data.”

He also expressed support for the earliest consumer genetic tests, despite their imperfections: “sometimes you’d get two results with two tests,” he observed – but added, that’s “ok,” both consumers and doctors will figure it out, and learn to calibrate appropriately. He was less concerned about imperfect information, and more concerned by the harm he believed would result from excessive regulatory requirements.

8. Points Of Light

The conference offered several presentations I found particularly encouraging– for instance, Bobby Jefferson’s talk about tech innovation to solve healthcare problems in developing countries, Peter Goodhand’s GA4GH update, and Jody Rank’s talk about block chain technologies (though I understood less than Ginger the dog in the Larson cartoon). Two talks on PMI – by Claudia Williams of OSTP, and Kathy Hudson of NIH – both made a strong case for why this project is particularly timely (versus ten years ago, when apparently NIH Director Francis Collins first floated the idea), and highlighted the tremendous progress made in the last year (see here).

I also wanted to acknowledge a particularly inspiring talk by Taha Kass-Hout about openFDA and precisionFDA; I won’t discuss this further due to a conflict (DNAnexus is the contractor for the precisionFDA work), but I would be remiss not to mention the presentation.

Wrap Up

Science is hard, biology is complex, disease is relentless – and neither Presidential proclamations nor transformative technologies alter these fundamental realities. The intrinsic worthiness, even nobility, of our collective ambition to understand and treat disease in a more individualized fashion doesn’t grant us the ability to achieve and deliver this.

Nevertheless, the promise of precision medicine – mediated by improved data sharing and sophisticated analytics – remains sound. The ambitious organizations – public and private -- who have taken on the challenge of translating precision medicine from slogan to practice will be the ones who work through the problems, and figure out the solutions. How rapidly we get there, however, is likely to depend on our ability to learn how to share data and insights more effectively.